pyspark-nested-functions 0.1.5

Utility functions to manipulate nested structures using pyspark

Nested fields transformation for pyspark

Motivation

Applying transformations for nested structures in spark is tricky. Assuming we have JSON data with highly nested structure:

[
  {
    "data": {
      "city": {
        "addresses": [
          {
            "id": "my-id"
          },
          {
            "id": "my-id2"
          }
        ]
      }
    }
  }
]

To hash nested "id" field you need to write following spark code

import pyspark.sql.functions as F

hashed = df.withColumn("data",
                       (F.col("data")
                        .withField("city", F.col("data.city")
                                   .withField("addresses", F.transform("data.city.addresses",
                                                                       lambda c: c.withField("id",
                                                                                             F.sha2(c.getField("id"),
                                                                                                    256)))))))

With the library the code above could be simplified to

import pyspark.sql.functions as F
from pyspark.sql.types import StringType
from nestedfunctions.functions.terminal_operations import apply_terminal_operation

hashed = apply_terminal_operation(df, "data.city.addresses.id", lambda c, t: F.sha2(c.cast(StringType()), 256))

Instead of dealing of nested transformation functions you could specify terminal operation as 'lambda' and field hierarchy in flat format and library will generate spark codebase for you.

Install

To install the current release

$ pip install pyspark-nested-functions

Available functions

Add nested field

Adding a nested field called new_column_name based on a lambda function working on the column_to_process nested field. Fields column_to_process and new_column_name need have the same parent or be at the root!

from nestedfunctions.functions.add_nested_field import add_nested_field
from pyspark.sql.functions import when
processed = add_nested_field(
      df,
      column_to_process="payload.array.booleanField",
      new_column_name="payload.filingPacket.booleanFieldAsString",
      f=lambda column: when(column, "Y").when(~column, "N").otherwise(""),
  )

Date Format

Format a nested date field from to current_date_format to target_date_format.

from nestedfunctions.functions.date_format import format_date
date_formatted_df = format_date(
      df,
      field="customDimensions.value",
      current_date_format="y-d-M",
      target_date_format="y-MM"
  )

Drop

Recursively drop fields on any nested level (including child)

from nestedfunctions.functions.drop import drop

dropped_df = drop(df, field="root_level.children1.children2")

Duplicate

Duplicate the nested field column_to_duplicate as duplicated_column_name. Fields column_to_duplicate and duplicated_column_name need have the same parent or be at the root!

from nestedfunctions.functions.duplicate import duplicate
duplicated_df = duplicate(
      df,
      column_to_duplicate="payload.lineItems.comments",
      duplicated_column_name="payload.lineItems.commentsDuplicate"
  )

Expr

Add or overwrite a nested field based on an expression.

from nestedfunctions.functions.expr import expr
field = "emails.unverified"
processed = expr(df, field=field, expr=f"transform({field}, x -> (upper(x)))")

Field Rename

Rename all the fields based on any rename function.

from nestedfunctions.functions.field_rename import rename
def capitalize_field_name(field_name: str, suffix: str) -> str:
  return field_name.upper()
renamed_df = rename(df, rename_func=capitalize_field_name())

Fillna

This function mimics the vanilla pyspark fillna functionality with added support for filling nested fields. The use of the input parameters value and subset is exactly the same as for the vanilla pyspark implementation.

from nestedfunctions.functions.fillna import fillna
# Fill all null boolean fields with False
filled_df = fillna(df, value=False)
# Fill nested field with value
filled_df = fillna(df, subset="payload.lineItems.availability.stores.availableQuantity", value=0)
# To fill array which is null specify list of values
filled_df = fillna(df, value={"payload.comments" : ["Automatically triggered stock check"]})
# To fill elements of array that are null specify single value
filled_df = fillna(df, value={"payload.comments" : "Empty comment"})

Flattener

Return flattened representation of the data frame.

from nestedfunctions.spark_schema.utility import SparkSchemaUtility

flatten_schema = SparkSchemaUtility().flatten_schema(df.schema)
# flatten_schema = ["root-element",
#                   "root-element-array-primitive",
#                   "root-element-array-of-structs.d1.d2",
#                   "nested-structure.n1",
#                   "nested-structure.d1.d2"]

Hash

Replace a nested field by its SHA-2 hash value. By default the number of bits in the output hash value will be 256 but a different value can be set.

from nestedfunctions.functions.hash import hash_field
hashed_df = hash_field(df, "data.city.addresses.id", num_bits=256)

Nullify

Making field null on any nested level

from nestedfunctions.functions.nullify import nullify

nullified_df = nullify(df, field="creditCard.id")

Redact

Replace a field by the default value of its data type. The default value of a data type is typically its min or max value.

from nestedfunctions.functions.redact import redact
redacted_df = redact(df, field="customDimensions.metabolicsConditions")

Whitelist

Preserving all fields listed in parameters. All other fields will be dropped

from nestedfunctions.functions.whitelist import whitelist

whitelisted_df = whitelist(df, ["addresses.postalCode", "creditCard"]) 

License

Apache License 2.0